Don't be intimidated by experimentation. Sure, it sounds complicated -- test tubes bubbling away in a perfect arrangement while people in lab coats jot down notes. And yes, it can sound expensive -- hundreds of man hours, expensive equipment, possibly specialized consultants or labs.
In reality, experimenting isn't expensive or difficult. Every product-based company and startup should embrace basic research principles, as there are a lot of good cues that businesses can take from researchers working in the hard sciences. Here are a few basic (and proven!) principles to start with:
1. Have a product hypothesis.
You may have learned it in elementary school, but having a working hypothesis is a bit harder than it seems. Say you're a product developer and you have a technology for smarter, faster bluetooth connections. Your first instinct will be to explore all of the applications of that sweet new technology and look for the most useful and profitable ones.
But before you begin, bring a hypothesis to the table. Exploring a ton of information without a grounding research question or theory can make it difficult to know if you did a good job, or even when you should stop exploring.
A hypothesis is an essential rudder when looking for your best course of action. Make a statement that can be proven or disproven, and before you start your experiment - make sure either answer would provide valuable next steps for the team.
2. Search wide, then dive deep.
Before you invest a lot of time in a single area -- say, heavily targeting a specific audience or adding a specific set of features -- it's critical to know you're on the right path. One of the most common mistakes I see people make: Their intuition kicks in and they go with their first idea, then only explore that option.
Take a large set of options, be they design decisions or market strategies, then test them against some of the best criteria you can measure. Then, and only then, will you have the confidence that an experiment is working or not.
In our hypothetical super-bluetooth example, we would pick a criteria like potential market size, then consider different industries like medical devices and consumer electronics against each other. We may all emphatically believe that the applications for smartphones will make consumer electronics the true winner, but we won't know until we explore how exactly how many pacemakers or hearing aids need our super-bluetooth.
After testing a broad set of options, you'll be left with a smaller and more manageable set of categories to explore in order to move your project forward.
3. Fail hard and often.
If you're approaching decisions and problems with a hypothesis, then doing a fair and rigorous job at testing them, at some point you should find yourself disproving an assumption -- being wrong. This is essential to a good experiment; it proves you're serious about finding the right answer and not validating your own assumptions into an echo chamber.
If you're just dipping your toes, it won't hurt to go with some relatively binary tests at first, looking at just X and Y. Eventually, a well-designed experiment will test a number of different hypothesis and variables at once, hopefully giving more invalidated assumptions than validated ones as the experiment goes on.
In these cases, being wrong is exceptionally valuable; it closes off a worthless course of action and frees you up to dive deeper on a more valuable one. Once we prove that our super-bluetooth is best suited for the consumer electronics industry, we should feel comfortable being wrong as often as possible when looking for the first devices and applications to pursue first.
There's a classic quote from inventor-mogul Thomas Edison that gets referenced in these sort of lessons that I'd like to leave you with -- here's the most accurate version I could find, published in American Magazine:
"After we had conducted thousands of experiments on a certain project without solving the problem, one of my associates, after we had conducted the crowning experiment and it had proved a failure, expressed discouragement and disgust over our having failed to find out anything. I cheerily assured him that we had learned something. For we had learned for a certainty that the thing couldn't be done that way, and that we would have to try some other way."
Designers, app developers, marketers and business leaders -- don't be afraid to experiment more scientifically. You can apply these techniques to a massive portion of the decisions you make without a lab coat, without big data, and without a big budget.